Systems, methods, and devices for generation of peer validated geospatial and proof of reception of tracking data

ABSTRACT

Described herein are systems and methods for validating received encrypted first data including position data and time data for a transportation vehicle. The system receives or accesses second data including second position and second time data of the transportation vehicle. The system determines a validity of the first data by performing operations on the encrypted first data or the encrypted first data and the second data to compare the encrypted first data and the second data. The system assigns a consensus score to the mining device based part on the comparison, and applies a signature function to the encrypted first position and first time data. The system then publishes the encrypted signed valid first position and first time data to a public transportation vehicle ledger.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 63/295,417, filed on Dec. 30, 2021, the content of which is herebyincorporated by reference in its entirety.

BACKGROUND OF THE DISCLOSURE

As airspace, marine, and terrestrial traffic becomes filled with varioustypes of vehicles from unmanned vehicles and drones to cargo ships andjet aircraft; for the safety and validity of assets, such as aircraft,the aircraft broadcasting its location will be critical to automatedsystems. Methods for flight and other vehicle routing and trafficmanagement, such as artificial intelligence or machine learning basedmethods, will require accurate data to generate predictions for allassets, such as flights historically and in real-time.

Real-time monitoring or access to historical geo-spatial transportationtracking information is possible using a variety of public and/orprivate sources. This data can be collected using a wide variety ofsensors at different scales of resolution in combination, and can becollected from publicly available web sites (e.g., with web scrapingmethods).

For the safety of the transportation industry, government regulatorssuch as the US Federal Aviation Administration (FAA) and the US CoastGuard have implemented requirements for the broadcasting of a vessel oraircraft's current position in real time. For example, the United Statesand many other countries require aircraft to transmit AutomaticDependent Surveillance Broadcast (ADS-B data). The ADS-B data includesan aircraft's global positioning system (GPS) location, altitude, groundspeed, and other data. As another example, large ships are required tobroadcast their position with an automatic identification system (AIS).These signals are unencrypted and can be received by nearby receivers inreal-time and recorded as historical geodata.

Online asset tracking website businesses have distributed low costsoftware defined radios capable of collecting, decoding and monitoringgeodata to consumers/users, thereby creating an additional nodes in thetracking businesses' networks, from which the consumer/user can opt toshare the collected and decoded geodata in real-time from vessels oraircraft over the internet to the website's central server. The hardwareof many software defined radios are based upon an open-source design,along with open-source firmware to decode the raw signals such as 1090MHz for ADS-B and 978 MHz or Marine AIS into positional data. Inaddition to these “localized” radio frequency reception oftransportation geodata, FAA and some other regulators provide real timeaccess to PUBLIC data streams over the internet. For example, the FAASystem Wide Information Management (SWIM) program provides informationvia the SWIM Industry-FAA Team (SWIFT) web site, which can be used forflight tracking to provide real-time access to data from airports suchas radar, weather, ground traffic, etc.

Some online tracking businesses' websites such as Flightaware,flightradar24, etc. provide privatized live tracking of airplanes. Allsources, such as Flightaware, that opt to utilize FAA SWIM data, such asADS-D data required to be broadcast by aircraft, are contractuallyrequired to follow FAA regulations limiting aircraft data displayed(LADD) rules and obfuscate the data of certain aircraft from publicviewing, which can cause problems for automated systems making decisionsfrom these websites based upon geodata across all industries. Variousassets from all types of geodata are deliberately or accidentallyobfuscated which can cause time-series calibration errors for theseautomated systems.

In some cases for airplanes, the prediction capability of the systemwill require access to obfuscated flight plans and identifiers toeffectively route aircraft. Aircraft with obfuscated tracking signalssuch as dynamic HEX-ID which can be a code that is associated with aregistration of the aircraft that changes each time the takes off andlands, or the FAA LADD program can cause errors for automated orartificial intelligence systems attempting to determine optimal flightroutes.

The proper operation of automated systems monitoring geodata and theeconomic value itself of all ADS-B nodes operating correctly is criticalto these flight tracking websites and their users, but the informationprovided by flight tracking websites can be vulnerable to direct hackingattacks, such as denial of service or spoofing, and inherent websitefailures such as the Facebook DNS shutdown in 2021. Although the ADS-Btracking websites rely upon their network of users to keep receiversconnected to the internet and operating, these users are not compensatedmeritoriously for the quantity or unique data their receiver collectsand shares using mostly open-source hardware and software components.

The management of privacy laws is a difficult task for human operatorsto perform manually, but can be difficult to automate due to the risk oferrors and potential litigation. Privacy Laws in various jurisdictions,such as the California Consumer Privacy Act (CCPA), prevent breaches ofgeolocation data from individuals. Due to these risks of potentiallitigation, public databases which comprise potential privacy data, suchas FAA LADD data, are often incomplete to protect individual's privacy.

Artificial intelligence systems such as risk management, trafficrouting, etc. are built upon the training and testing on validated data,and if there is a missing portion of geodata such as obfuscatedaircraft, vessels, and unmanned vehicles; this can cause errors in thepredictive capabilities of such autonomous systems. Yet, it is criticalfor the privacy of these assets to remain intact, while remainingpossible to integrate these obfuscated assets into the automatedworkflows of traffic planning, prediction, and management.

Although privacy systems put in place by regulators, such as the LADDsystem, are meant for privacy and safety, it is also used by maliciousentities such as dictators, criminal organizations, and state actors toobfuscate their illicit movements.

Some permissionless blockchains and proof of coverage systems have beendeveloped for deploying hardware democratically by compensating usersfor providing “Coverage” of RF signals in the 910 MhZ band (LoRaWan),however “proof of coverage” requires the transmission of encryptedsignals to provide security and trust to the network, which itself isvulnerable to hardware tampering and does not allow for a transparentconsensus mechanism for the network. In some conventional permissionlessblockchain systems for proof of coverage systems, the mining hardware isproprietary to specific companies, and they alone hold the validationkeys for new nodes to be added to the blockchain. There is a need forsecure off the shelf hardware to self-validate and become part of thenetwork, while mitigating malicious or erroneous data from nodes beingadded to the blockchain.

Although a blockchain is immutable and protected from historicalmodification, the adding of data to the blockchain is only as secure asthe hardware/software that contributes this packetized information tothe network and subsequent blockchain. In relation to real time datarecording, this can result in spoofed or erroneous data being written tothe blockchain, without a way to efficiently remove the data and anyassociated value earned by the false entity from the ledger.

Some conventional permissionless blockchain systems for proof ofcoverage systems have been vulnerable to gaming of the system whereusers acquire multiple hardware devices and strategically spoof theirlocation in order to fraudulently “provide coverage” to a geographicalarea. This spoofing and subsequent theft occurs daily and cannot bereversed from the blockchain effectively. This gaming of compensationcan be performed geographically anywhere on the planet through GPSspoofing, and strategic positioning of hotspots in proximity to eachother, without providing any actual “coverage” to the blockchain.

Proof of Coverage systems based upon encrypted signals are especiallyvulnerable to hardware tampering, cloning, or side channel and man inthe middle attacks. The hardware tampering can be either the spoofing ofcoverage or in addition to probing of hardware through contactlessoptical probing enabling users to drastically modify the hardware'sbroadcast power, break encryption, or write unvalidated data to theblock-chain. For a quantum system to accurately model and spoof the datagenerated by the aggregate randomness of the estimated 40,000 dailymovements of FAA regulated aircraft with complete accuracy would beimpossible.

Proof of Work systems such as Nakamoto's Bitcoin are less vulnerable tohardware and software attacks as the algorithm for contributing to thenetworks is completely democratic, but can be dissolved through quantumsupremacy.

Regulators set in place the standards such as ADS-B for aircraft, AISfor Marine vessels, Remote ID for unmanned drones; while there stillremains no standards or automated methods of peer consensus forvalidating this trusted real-time geodata and compensating users forproviding valid “proof of reception” of an asset's regulatorybroadcasts.

If enough software defined radio hardware is deployed across the groundunderneath an airspace, a decentralized network of the software definedradio hardware can be created that can be used to track the position andtime of transportation vehicles as they move throughout space. A privatecorporation is building, installing and maintaining a nationwide networkof ADS-B ground receivers in the United States. Typically, these groundreceivers are located at, near, or are in direct communication withairports, as the system has an effective operating range of roughly 100to 150 miles. Since approximately 2015, ground receivers sufficient toprovide coverage for virtually the entire airspace over the continentalU.S. have been in place.

However, as drone delivery and unmanned vehicle logistics becomeintegrated into daily life, there will be a finer resolution of coveragerequired across the entire United States, not just where airports arepresent in order to accurately and safely monitor drone and unmannedvehicle movements in real-time across states, cities, and neighborhoods.

Drones will fly at lower altitudes, while unmanned terrestrial vehicleswill have even further reduced broadcasting range; and therefore thecurrent coverage and reception for general and commercial aviation willbe wholly inadequate for providing thorough coverage.

Meanwhile drones are lighter in weight and limited in their hardwarepayload; and this will decrease the overall broadcast range of theonboard electronics possible, thereby increasing the ground-basedreception requirements for receivers. Deploying reliable radio frequencyhardware is expensive, difficult, and can be wasteful if not optimizedfor geography, population, and noise; electronics designs are oftenbased upon ASIC devices and are limited in their ability to bereconfigured for new applications. New systems for trackingtransportation vehicles are needed.

SUMMARY

A proof of reception of tracking data system is presented. The systemincludes a mining device that includes an antenna coupled to a softwaredefined radio, and a first processor configured to or programmed to readone or more instructions held in memory to encrypt first data includingfirst position and first time data for a transportation vehicle, thefirst position and first time data obtained from at least one signalemitted from the transportation vehicle and received via the antenna.The processor is also configured to or programmed to transmit theencrypted first data for validation. The system also includes avalidation device, the validation device including a second processorconfigured to or programmed to read one or more instructions held inmemory to receive the encrypted first data from the mining device viathe communication interface, receive or access second data includingsecond position and second time data for the transportation vehicle; anddetermine a validity of the first data by performing operations on theencrypted first data or on the encrypted first data and the second datato compare the encrypted first data and the second data. The secondprocessor can be configured or programmed to determine a validity of thefirst position and first time data based at least in part on thecomparison of the first data and the second data, assign a consensusscore to the mining device based at least in part on the validity of thefirst position and first time data, apply a signature function to theencrypted first position and first time data, where the first positionand first time data is determined to be valid, to obtain signed validencrypted first position and first time data, and publish the signedvalid encrypted first position and first time data to a publictransportation vehicle ledger. A validation device is presented. Thevalidation device includes a processor configured to or programmed toread one or more instructions held in memory to receive encrypted firstdata from a mining device, the first data including first position andfirst time data for a transportation vehicle. The processor is furtherconfigured to receive or access second data including second positionand second time data for the transportation vehicle. The processor isfurther configured to determine validity of the first data by performingoperations on the encrypted first data or on the encrypted first dataand the second data to determine a relationship between the first dataand the second data. The processor is further configured to determine avalidity of the first data by performing operations on the encryptedfirst data or on the encrypted first data and the second data to comparethe encrypted first data and the second data. The processor is furtherconfigured to assign a consensus score to the mining device based atleast in part on the comparison of the first data and second data. Theprocessor is further configured to apply a signature function to theencrypted first position and first time data, where the first positionand first time data is determined to be valid, to obtain encryptedsigned valid first position and first time data. The processor isfurther configured to publish the encrypted signed valid first positionand first time data to a public transportation vehicle ledger.

A non-transitory computer-readable medium is presented. Thenon-transitory computer-readable medium stores computer-executableinstructions which when executed by at least one processor, cause the atleast one processor to perform the operation of receiving encryptedfirst data from a mining device, the first data including first positionand first time data for a transportation vehicle. Thecomputer-executable instructions further cause the at least oneprocessor to perform the operation of receiving or accessing second dataincluding second position and second time data for the transportationvehicle. The computer-executable instructions further cause the at leastone processor to perform the operation of determining validity of thefirst data by performing operations on the encrypted first data or onthe encrypted first data and the second data to determine a relationshipbetween the first data and the second data. The computer-executableinstructions further cause the at least one processor to perform theoperation of determining a validity of the first data by performingoperations on the encrypted first data or on the encrypted first dataand the second data to compare the encrypted first data and the seconddata. The computer-executable instructions further cause the at leastone processor to perform the operation of assigning a consensus score tothe mining device based at least in part on the comparison of the firstdata and second data. The computer-executable instructions further causethe at least one processor to perform the operation of applying asignature function to the encrypted first position and first time data,where the first position and first time data is determined to be valid,to obtain encrypted signed valid first position and first time data. Thecomputer-executable instructions further cause the at least oneprocessor to perform the operation of publishing the encrypted signedvalid first position and first time data to a public transportationvehicle ledger.

A method for validating a device is presented. The method includesreceiving encrypted first data from a mining device, the first dataincluding first position and first time data for a transportationvehicle. The method includes receiving or accessing second dataincluding second position and second time data for the transportationvehicle. The method includes determining validity of the first data byperforming operations on the encrypted first data or on the encryptedfirst data and the second data to determine a relationship between thefirst data and the second data. The method includes determining avalidity of the first data by performing operations on the encryptedfirst data or on the encrypted first data and the second data to comparethe encrypted first data and the second data. The method includesassigning a consensus score to the mining device based at least in parton the comparison of the first data and second data. The method includesapplying a signature function to the encrypted first position and firsttime data, where the first position and first time data is determined tobe valid, to obtain encrypted signed valid first position and first timedata. The method includes publishing the encrypted signed valid firstposition and first time data to a public transportation vehicle ledger.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the present invention will bemore fully understood by reference to the following detailed descriptionin conjunction with the attached drawings in which like referencenumerals refer to like elements throughout the different views.

FIG. 1 is a flow chart for validating received transportation vehicledata, in accordance with some embodiments of the disclosure.

FIG. 2 is a flow diagram illustrating a process of generatinghomomorphically encrypted transportation vehicle data and publishing thehomomorphically encrypted transportation vehicle data to a publicledger, in accordance with some embodiments of the disclosure.

FIG. 3 is a flow diagram illustrating a process of validatinghomomorphically encrypted transportation vehicle data, in accordancewith some embodiments of the disclosure.

FIG. 4 schematically depicts a network for transmitting and receivinghomomorphically encrypted transportation vehicle data, in accordancewith some embodiments of the disclosure.

FIG. 5 is a flow diagram illustrating a process of validatinghomomorphically encrypted transportation vehicle data and generating aconsensus score for a mining device, in accordance with some embodimentsof the disclosure.

FIG. 6 is a flow diagram illustrating a process of applying an operationto homomorphically encrypted transportation vehicle data, in accordancewith some embodiments of the disclosure.

FIG. 7 is a flow diagram illustrating a process of validatinghomomorphically encrypted transportation vehicle data, in accordancewith some embodiments of the disclosure.

FIG. 8 is an image of a prototype mining device, in accordance with someembodiments of the disclosure.

FIG. 9 is a flow diagram illustrating a process of receiving unencryptedtransportation vehicle data from a sensor at a mining device andtransmitting homomorphically encrypted transportation vehicle data tovalidator nodes in a consensus network, in accordance with someembodiments of the disclosure.

FIG. 10A is a flow diagram illustrating a deep leaning reinforcementprocess for analyzing real-time transportation vehicle data to predicttrajectory of a transportation vehicle, in accordance with the someembodiments of the disclosure.

FIG. 10B is a flow diagram illustrating a process of training a neuralnetwork to analyze real-time transportation vehicle data to predicttrajectory of the transportation vehicle, in accordance with the someembodiments of the disclosure.

FIG. 11 is a flow diagram illustrating a process for integratingreal-time transportation vehicle artificial intelligence data with andpublicly available data to generate a fingerprint associated with atransportation vehicle, in accordance with some embodiments of thedisclosure.

FIG. 12A is a flow diagram illustrating the process of automaticallygenerating an asset token, in accordance with some embodiments of thedisclosure.

FIG. 12B is a flow diagram illustrating the process of manuallygenerating an asset token, in accordance with some embodiments of thedisclosure.

FIG. 13 is a flow diagram illustrating a process of validatinghomomorphically encrypted transportation vehicle data and publishing thevalidated homomorphically encrypted transportation vehicle data to apublic ledger, in accordance with some embodiments of the disclosure.

FIG. 14 is a flow chart for processing position and time data at amining device, in accordance with some embodiments of the disclosure.

FIG. 15 schematically depicts an example computing module of thesurgical robotic system in accordance with some embodiments.

DETAILED DESCRIPTION

While various embodiments of the invention have been shown and describedherein, it will be clear to those skilled in the art that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions may occur to those skilled in the art withoutdeparting from the invention. It may be understood that variousalternatives to the embodiments of the invention described herein may beemployed.

As used in the specification and claims, the singular form “a,” “an,”and “the” include plural references unless the context clearly dictatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” or “include” and/or “including,” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof. As used herein,the term “and/or” includes any and all combinations of one or more ofthe associated listed items.

Conventional tracking of transportation vehicles has been via expensivesoftware that is heavily customized for very specific purposes and canonly can track one type of transportation vehicle. For example, aircraftare conventionally tracked using expensive ground receivers, such asairport surveillance radar, that are specifically designed to collectaircraft position and time data only. They are secure and difficult tohack or tamper with. Satellites are also used at times when the aircraftis in airspace where there no ground receivers as in the case when theaircraft is traveling over a large body of water. The satellites arealso secure and difficult to hack or tamper with. Further, the positionand time data of the aircraft are broadcast by the aircraft onfrequencies that are reserved solely for tracking aircraft, and so theground receivers and satellites receivers are tuned to receive signalsoperating at the frequencies for tracking aircraft.

In the case of ship, boat, or other marine vessels there are also groundbased receivers that can receive position and time data associated withthe ship, boat, or marine vessels in addition to GPS data. However theseground based receivers only receive position and time data associatedwith the ship, boat, or marine vessels on a frequency that is designatedfor receiving position and time data for ships, boats, or marinevessels.

Because both marine and aircraft tracking systems have receivers thatare specifically tuned operate on frequencies to track marine vesselsand aircraft, and because there are so few of them in the world, thecost to operate two separate systems can become prohibitively expensive.An alternative would be a receiver that is tuned to receive signals thatoperate within the frequency ranges that aircraft position and time dataare transmitted on and the frequency ranges that marine vessel positionand time data are transmitted on, and which is also less expensive todeploy and operate than the conventional tracking systems for aircraftand marine vessels. Software defined radios that can receive theposition and time data on the frequencies that aircraft and marinevessels transmit tracking data on can be deployed relativelyinexpensively and in greater number that conventional receiverscurrently used to track aircraft and marine vessels. Hundreds tothousands software defined radios can be deployed across a geographicregion thereby providing better resolution and more accurate informationbecause of the density of the number of software defined radios in thegeographic area. Because the software defined radios can be deployed inlarge numbers across a geographic area, they can form a decentralizednetwork where each of the software defined radios in the decentralizednetwork can communicate with one another.

Not only does the decentralized network provide redundancy should asoftware defined radio fail, but it also provides a means by which aprocessor (e.g. a reconfigurable processor), that is communicativelycoupled to a software defined radio, can not only process position andtime data received from aircraft but also position and time data frommarine vessels and drones. Because the software defined radios form adecentralized network, and they each have ability to communicate witheach other, the processors that are communicatively coupled to thesoftware defined radios, they can compare the position and time datathat they each receive to other position or time data that they receivefrom peer software defined radios. By doing this, the decentralizednetwork of software defined radios can validate the position and timedata that is being received and shared with other software definedradios in the decentralized network is accurate. Such a decentralizednetwork would not only be more resilient, but it would also be costeffective and provide a means by which the position and time data thatis being received from aircraft and marine vessels can be validatedreal-time to ensure that the aircraft or marine vessel is accuratelybeing tracked. The combination of the software defined radio and theneuromorphic processor can form what is called a mining device inaccordance with some embodiments.

Some embodiments provide an automated system for tracking data thatdetermines what data should be protected and is not subject to humanerror, and provides additional protections for the database of sensitivedata. Privacy Preserving Neural Networks (PPNNs) using HomomorphicEncryption (HE) techniques, or other privacy retention systems such asfederated learning, zero knowledge proofs, can enable the analysis ofthis public sensitive data without compromising the privacy ofindividuals in accordance with some embodiments.

Some embodiments employ neuromorphic hardware that is capable ofreprogramming its operation after deployment to the field, iterativelyimproving the hardware configuration, and possess the inherent securityof design which can reconfigure itself upon sensing hardware or softwaretampering. Neuromorphic computing platforms such as IBM's TrueNorthprocessor could also be used to perform the same operations as an ASICbut with higher efficiencies in accordance with some embodiments.

As the computational requirements of the future A.I. applications willrequire parallel processing, validated nodes with spare neuromorphiccomputing resources can be used for data offload. Techniques such as FogComputing of Fog Computing as a Service with require trusted computationwithout compromising the privacy of data, but with a trusted centraldatabase or ledger to view, modify, and add to the ledger or blockchainin accordance with some embodiments.

As quantum computing and Shor's algorithm threatens the future ofcryptography and subsequently blockchain consensus mechanisms, there isa need for consensus mechanisms not based upon encryption alone, whichare non-deterministic. Deterministic methods and systems are vulnerableto being overtaken by quantum computing techniques, whilenondeterministic systems have inherent randomness and thereby areresistant to quantum computing “attacks”. The inherent randomness ofregulatory aircraft and marine vessels on a daily basis could provide anon-deterministic set of data that can be used for validating peerconsensus.

There are many peer consensus methods for validating nodes in thenetwork such as “Delegated” Proof of adaptions, Proof of Authority,Proof of Trust, Proof of Rec, Proof of Stake, Proof of Elapsed Time,Proof of Bandwidth, practical Byzantine Fault Tolerance (pBFT),Blockchain reputation based consensus (BRBC), etc. BRBCs are unlike mostof the existing consensus mechanisms, however, are constrained by lowefficiency and high energy consumption. For example, the BlockchainReputation-Based Consensus (BRBC) mechanism is through which a node musthave the reputation score higher than a given network trust thresholdbefore being allowed to insert a new block in the chain. Arandomly-selected set of judges monitors the behaviour of each nodeinvolved in the consensus and updates the node reputation score. Everycooperative behaviour results in a reward, and a noncooperative ormalicious behaviour results in a punishment. BRBC also uses thereputation score to revoke access to nodes with a reputation score belowa given threshold.

In some embodiments described herein, a “Proof of Reception” basedsecure tamper resistant hardware blockchain system is based upon peervalidation from unencrypted regulatory signals such as those fromcommercial aircraft to be able to provide a trusted framework fordecentralized open-source asset tracking with secure hardware. In someembodiments, users can participate in a decentralized non-fungibletokens marketplace based upon the minting of tokens from validatedgeospatial data.

In some embodiments, Proof of Work, Stake, Authority, Trust, Reputationcan all be enhanced through Proof of Reception by providing additionalvalidation through cryptographic and peer consensus by verifying realtime signals from the trusted ADS-B, AIS, Remote ID, etc. networks.These real time signals are detectable using inexpensive off the shelfhardware, but are often stronger than GPS signals, and themselves can beused for triangulation using transmissions from at least three (3)sources, while remaining less susceptible to spoofing due to the dynamicnature of aircraft, marine, or drone positional broadcasts. In regionswhere GPS signal is weak or threatened by spoofing, but positionalbroadcasts from ADS-B, AIS, or Remote ID are prevalent, this form oftriangulation can be more accurate and secure.

Some embodiments provide a proof of reception of tracking data systemthat includes mining devices with software defined radios to receivesignals emitted from transportation vehicles including position and timedata, and that encrypt the position and time data and transmit theencrypted position and time data for validation. The proof of receptionof tracking data system also includes a validation device that receivesthe encrypted position and time data from the mining device and receivesor accesses second position and time data, and performs operations onthe encrypted position and time data and the second position and timedata to determine the validity of the encrypted and assign a consensusscore to the mining device based at least in part on the encryptedposition and time data. The validation device can apply a signaturefunction to the validated first position and time data and publish thesigned valid encrypted position and time data to a public transportationledger. Some embodiments provide a validation device for validatingencrypted position and time data received from a mining device. Someembodiments provide a mining device including a software defined radiothat generated encrypted (e.g., homomorphically encrypted) position andtime data for a transportation vehicle based on a radio signal receivedfrom the transportation vehicle. In some embodiments, the mining devicehas passive and/or active features to deter or detect tampering with thereceived position and time data.

FIG. 1 illustrates the process 100 for validating encrypted position andtime data received from a mining device that has been selected tovalidate the received position and time data. This mining device can bereferred as a validation node or validation device. At block 12 thevalidation device can receive encrypted first data associated with thetransportation vehicle from a mining device that has received one ormore signal including first data that includes first position and firsttime data. A processor in the mining device can encrypt the first datathat includes first position and first time data of the transportationvehicle that has been received on an antenna that is coupled to theprocessor in the mining device. After the validation device receives theencrypted first data, the validation device can receive second dataincluding second position and time data of the transportation vehicle atblock 14. The second data can be received before or after the first datais received. After receiving the second data the validation device candetermine whether the first data is valid by performing an operation onthe first encrypted data and the second data (block 16). The result ofthe comparison can be that the first position and time data is identicalto the second data, that the first position and time data is consistentwith the second data, or that the first position and time data isinconsistent with the second data. In some embodiments, the second datacan include multiple different data sets obtained from multipledifferent sources (e.g., from multiple different other mining devices).

After the validation device has determined that the first data is validas a result of performing the operation on the first encrypted data andthe second data, the validation device can then apply a signature atblock 18 to the encrypted first position data and the first time data.The signature applied to the encrypted first position data and the firsttime data indicates that the first data is valid. The validation devicecan then publish the signed encrypted first position and first time datato a public ledger at block 20.

Some collected data can be analyzed with methods capable of detectingtext and objects in imagery, video, and webpages. In some embodiments,these methods for detecting test and objects in imagery, video andwebpages can include artificial intelligence methods, for example,convolutional neural networks, deep learning, etc.

Additionally, transportation assets often have markings or registrationnumbers such as license plates, vessel name, etc. This information canbe obtained from an image of the transportation asset processed by imageprocessing, machine learning, template matching, etc. and can becombined with GPS data from device that obtained the image to create ageo data tag of the asset, thereby, creating a timestamped imagecorrelating to position, which can be aggregately used to provide amethod to correlate objects in physical space to a record in digitalform.

The advent of the internet of things has enabled access to low costdevices capable of collecting, decoding, and monitoring geodata usinghardware such as software defined radios, cameras. High resolutioncameras are available at low cost in combination with processors able toanalyze imagery and video data in realtime, while Lidar sensors arestill expensive but are commercially available.

FIG. 2 illustrates a system 200 for publishing homomorphically encryptedtransportation vehicle data to a public ledger in accordance with someembodiments. The system 100 can include antenna 108 that detects orreceives one or more transportation vehicle signals associated withAutomatic Dependent Surveillance-Broadcast (ADS-B) data 102, Remote IDBluetooth Drone Data 104, and/or Marine Automatic Identification System(AIS) data 106 in accordance with some embodiments. The transportationvehicle signals can include signals associated with other or additionalsources in some embodiments. In countries other than the United States,the signals detected or received may have a different format and may beassociated with different entities or different systems. The ADS-B data102, Remote ID Bluetooth Drone Data 104, and Marine AIS data 106includes position and time information associated airplanes operating indifferent airspaces across the globe, drones operating in differentairspaces across the globe, and ships operating in different maritimewaters across the globe.

The signals associated with the ADS-B data 102 can be detected by theantenna 108 on a frequency of 978 or 1090 MHz, the signals associatedwith the Remote ID Bluetooth Drone Data 104 can be detected by theantenna 108 within a frequency range of 2400 to 2483.5 MHz, and thesignals associated with the Marine AIS 106 data can be detected by theantenna 108 on a frequency of frequency of 161.975 MHz and 162.025 MHz.These frequency ranges may be different for different countries. Thisreceived data (e.g., transportation vehicle data) may be described asasset tracking data, and is based on the raw unencrypted transmissionsof these assets (e.g., transportation vehicles) and their positionaldata which can be collected by antennas of the required frequency. Insome embodiments, the antenna can be communicatively coupled to aweather proof coaxial wireless or wired transmitter 110 that convertsthe received signals into a coaxial wired signal or wireless signal thatcan be transmitted to a wireless or coaxial wired connection 118 of amining device (black box miner 120).

In some embodiments, the black box miner 120 can include a tamper-proofenclosure and design that can detect when the black box miner 120 isbeing opened or one or more components therein have been modified orremoved.

In some embodiments, the black box miner 120 can include a processor ormultiple processors that can execute a democratic mining algorithm thatcauses processor(s) to apply a homomorphic encryption algorithm to theposition and time data associated with the ADS-B data 102, Remote IDBluetooth Drone Data 104, and Marine AIS data 106 so that the data canbe analyzed by other mining devices while preserving the privacy of theactual ADS-B data 102, Remote ID Bluetooth Drone Data 104, and MarineAIS data 106. Among the benefits of homomorphic encryption is thatmathematical operations can be performed on data that is homomorphicallyencrypted without decrypting the data. In some embodiments, theprocessor or processors of the black box miner that perform homomorphicencryption include a neuromorphic processor.

In some embodiments, the system 100 includes a user interface 116 userinterface associated with a computer, handset (mobile phone), othercomputing device and/or may include one or more of a monitor, button andlight emitting diodes (LES). Some examples of different interfaces aredepicted as a collection of different user interfaces 1210 in FIG. 13 .In some embodiments, the user interface can be used by a user to loadfirmware and software related to the democratic mining algorithm 114and/or one or more deep learning modules that can be executed by theprocessor (e.g., the neuromorphic processor) to predict one or moreactions associated with transportation vehicles (e.g., airplanes,drones, or ships) based on the received encrypted data (e.g., ADS-B data102, Remote ID Bluetooth Drone Data 104, and Marine AIS data 106).

The black box miner 120 can apply homomorphic encryption algorithm tothe received ADS-B data 102, Remote ID Bluetooth Drone Data 104, andMarine AIS data 106 thereby creating the homomorphically encrypted data,which may be incorporated into a smart contract. A smart contract isprogram, that may be stored on a blockchain, and that runs or executeswhen predetermined conditions are met. In some embodiments, thehomomorphically encrypted data associated with the received ADS-B data102, Remote ID Bluetooth Drone Data 104, and Marine AIS data 106 can bepublished to a ledger on a permissionless blockchain 124 after thehomomorphically encrypted data has been validated by a validator node.In some embodiments, after the homomorphically encrypted data has beenpublished to the ledger on the permissionless blockchain 124, othermining devices can purchase the homomorphically encrypted data foranalysis and to predict certain actions associated with thetransportation vehicles (airplanes, drones, or ships, e.g. vessels) thatgenerated the data.

FIG. 3 is a flow diagram illustrating the process of validatinghomomorphically encrypted transportation vehicle data, in accordancewith some embodiments of the disclosure. Process 300 includes miningdevice (node 206) which can be either a trusted mining device or auntrusted mining device. The node 206 can receive real time un-encryptedADS-B or Remote ID data 208 that has been broadcast by airplanes (e.g.,ADS-B data 102) or drones (e.g., Remote ID Bluetooth Drone Data 104).The real time un-encrypted ADS-B or Remote ID data 208 can includeposition and time data associated with the airplanes or drones when itis broadcast in accordance with some embodiments. In some embodimentsdata associated with the position and time at which the data isbroadcast by ships, boats, or vessels (e.g., Marine AIS data 106) can bereceived. In some embodiments, other types or formats of data from othertransportation vehicles may be received.

Although FIG. 3 and other example embodiments are primarily explainedwith respect to DS-B data, Remote ID data, or AIS data for illustrativepurposes, one of ordinary skill in the art in view of the presentdisclosure will understand that embodiments are not limited to ADS-Bdata, Remote ID data or marine AIS data, and that other types of dataincluding position data broadcast or transmitted from vehicles ortransportation vehicles may be employed in connection with anyembodiments.

In some embodiments, node 206 can decode the received data (e.g., ADS-Bor Remote ID data 208) on a RF front end processor, and the RF front endprocessor can send the decoded ADS-B or Remote ID data to theneuromorphic processor 202 in node 206. The neuromorphic processor 202can apply the homomorphic encryption mining algorithm 216 to the decodedreceived data (e.g. ADS-B or Remote ID data) after which theneuromorphic processor 202 can generate a public key, that can be usedby other mining devices to encrypt data that they can send to the node206, and a private key that the node 206 can use to encrypt the decodedADS-B or Remote ID based on the homomorphic encryption mining algorithm.The node 206 can store the private key 212 in a secure storage locationin the node 206 called a node wallet 210. The node 206 can thenbroadcast the public key 220 to all other mining devices that are a partof the same network, as well as the decoded received ADS-B or Remote IDdata.

A system can include a validator node 228, which is a mining device thathas been selected to validate that the received ADS-B or Remote ID data208 is correct and if there is network consensus. In some embodiments,the validator node 228 can receive the encrypted ADS-B or Remote ID datawhich can be expressed as HE Data (x) 222 and can perform operationsupon the HE Data (x) 222 to determine if the data has been spoofed,contains errors, or is deceptive in any way. The validator node 228determines if the HE Data (x) 222 is valid by comparing the HE Data (x)222 against data from “trusted” nodes, geographically nearby, whichpreviously, currently, or are expected to be in the trajectory of thetransportation vehicle that is being tracked, through the use ofelectromagnetic and time of flight simulation such as Monte Carlo orPoint Spread Function calculation in some embodiments. The validatornode 228 can also use “trusted” data from regulatory bodies such as theFederal Aviation Administration (FAA) System Wide Information ManagementIndustry-FAA Team (SWIFT) and other public data 216 system whichprovides public data about aircraft operations in some embodiments. TheSWIFT data can include take-off and landing data, ground traffic data,weather data, flight routing data associated with an airplane. Otherdata that is publicly available that can also be used to by thevalidator 228 to validate that the received ADS-B or Remote ID data 208is correct, is information scraped from indexed webpages (indexing ofADS-B websites 234) that include similar information to that ispublished in the SWIFT portal.

In some embodiments, through the use of predictive algorithms, theregulatory data can be used to estimate the expected ADS-B data valuesto be transmitted from given aircraft between takeoff and landing, orfor AIS, leaving and entering port through modelling, simulation, deeplearning agents, etc. In some embodiments, satellite imagery can also beused on a daily or weekly basis to provide further validation about theposition and time data collected by the node 206 as an airplane movesbetween airports, or as a ship, boat, or vessel moves between ports. Thetrajectories of transportation vehicles can be set within constraints oftraffic regulations such as FAA horizontal and vertical spacing todetermine a relative threshold for the probability of valid signals inaccordance with some embodiments. Other means of verification such asradar, lidar, optical, auditory, seismic, etc. can also be used toprovide validation.

In some embodiments, the validator node 228 can execute a proof ofreception trusted consensus 232 operation in which the validator node228 issues a consensus score to the node 206 based at least in part onwhether the ADS-B or Remote ID data that the node 206 has shared withthe validator 228 is correct. In some embodiments, the consensus scoreis based at least in part on the amount of data shared, adjusted by amultiplier which is calculated from the given transportation vehicle'svalue. The transportation vehicle value is based at least in part on theoperating resources (e.g., fuel) required by the transportation vehicleand the size of the transportation vehicle. For example, the operatingresources required to service and operate a large cargo airplane (e.g.,a Boeing 747) will be greater than the operating resources required toservice and operate a small crop dusting airplane (e.g., a Cessna 172).In some embodiments, the consensus score can also be a function of“uniqueness” of the transportation vehicle while it is in operation. Insome embodiments, the uniqueness can be based at least in part on anunplanned path that the transportation vehicle takes between a point oforigin and a point of destination. Yet in still in other embodiments,the uniqueness value can also be based at least in part on an emergencyassociated with the transportation vehicle. Further still in otherembodiments, the consensus score can be based at least in part on amanifest associated with the transportation vehicle. The consensus scoremultiplier associated with a commercial aircraft will be greater thanthe multiplier associated with that of private aviation due to thehigher level of regulatory scrutiny of commercial aircraft. Because ofthe additional scrutiny applied to commercial aircraft, the likelihoodwith which erroneous transmissions or faulty equipment will be generatedby commercial aircraft is lower than it would be for private aircraft.Also because commercial aircraft must adhere to a stringent operatingschedule, it is also less likely that erroneous data will be generatedbecause mining nodes, including validator nodes, will know the scheduledand estimated takeoff and landing times associated with commercialaircraft. In contrast, the scheduled and estimated takeoff and landingtimes associated with private aircraft can vary significantly, therebymaking it difficult to track and estimate position and time dataassociated with the private aircraft based on scheduled and estimatedtakeoff and landing times. Once the consensus score associated with thenode 206 exceeds a certain threshold, the node 206 can be considered atrusted node and can be utilized by other validator nodes to determineconsensus scores for untrusted nodes in the network in accordance withsome embodiments. The consensus score can change dynamically given thecurrent number of trusted and untrusted nodes operating along with thenumber of validators.

In some embodiments, if the ADS-B or Remote ID data that the node 206has shared with the validator 228 is determined to be invalid based on aconsensus amongst other trusted miner nodes, regulatory data analysisand simulation, and/or other consensus mechanisms, the consensus scoreof the node 206 can be decreased. The decrease in the consensus score isa penalty. The node 206 can be penalized and the corresponding consensusscore associated with the node 260 can decrease if there are directtrusted nodes refuting the validity of the ADS-B or Remote ID data. Thiscan be the most severe penalty. The amount by which the consensus scorecan decrease can be directly related to the amount of invalid datashared, as it results from spoofing or erroneous hardware.

The node 206 can be penalized and the corresponding consensus scoreassociated with the node 260 can decrease if the regulatory data doesnot match the expected value from the node 206. This penalty can beconsidered to be intermediate as it indicates invalidity. If there areno trusted nodes within proximity of node 206 that can be relied on toprovide ADS-B or Remote ID data that can be used to compare against theADS-B or Remote ID data received by node 206, however the regulatorydata matches with the ADS-B or Remote ID data received by node 206, thiswill have the least penalty on the consensus score for node 206.

In some embodiments, if node 206 has a consensus score that is above apredetermined threshold, and it is the only node receiving ADS-B orRemote ID data from an aircraft or drone, the ADS-B or Remote ID datawill not be published to the blockchain until it can be verified orverified. In this scenario, another trusted node that has the ability totriangulate a transportation vehicle (airplane or drone) that isproducing ADS-B or Remote ID data coinciding with a similar trajectoryto that of the aircraft or drone that the node 206 is tracking. In someembodiments, the ADS-B or Remote ID data that the node 206 has receivedcan be validated by one or more trusted nodes or validator nodes basedat least in part on simulation of regulatory data to determine if theADS-B or Remote ID data that node 206 has received actually matches thepossibilities of regulatory data.

In some embodiments, if the ADS-B or Remote ID data is not validated, itwill not yet be published to the public ledger or blockchain, but can beadded at a later time after validation. The ADS-B or Remote ID dataproduced by untrusted mining devices will only be published to thepublic ledger or blockchain if it can be validated using peer consensus,regulatory data simulation, or mining devices that are within ageographic proximity of the mining device generating the ADS-B or RemoteID data. This provides un-trusted mining device with the opportunity tohave the ADS-B or Remote ID data that it has received published to thepublic ledger or the blockchain by a validator node, while beingcompensated until the consensus score of the un-trusted mining deviceexceeds the predetermined threshold, and keeping the validity of thepublic ledger of the blockchain protected from spoofed ADS-B or RemoteID data or errors in ADS-B or Remote ID data. Mining devices that have aconsensus score that exceeds the predetermined threshold can bedesignated a validator node that an participate in a random peerconsensus election process in some embodiments.

If the ADS-B or Remote ID data that received by the node 206 isdetermined by the validator node 228 to be valid, the validator node 228will take the HE Data (x) 222 and apply a final HE validation signatureoperation f₁( ) 230 to the HE Data (x) 222 thereby producing f₁(HE Data(x) 222), indicating that the HE Data (x) 222 is valid, beforepublishing the signed HE Data (x) 222 to the public ledger or blockchain224. If the ADS-B or Remote ID data that received by the node 206 isdetermined by the validator node 228 to be invalid, the validator node228 will take the HE Data (x) 222 and apply a final HE validationsignature operation f₂( ) 230 to the HE Data (x) 222 thereby producingf₂(HE Data (x) 222), indicating that the HE Data (x) 222 is invalid,before publishing the signed HE Data (x) 222 to the public ledger orblockchain 224.

In some embodiments, the ledger or blockchain can be stored across thenetwork of mining devices in a decentralized manner. That is to say,that pieces of data recorded in the ledger, or blocks of the blockchain,are stored on different devices forming the decentralized network ofmining devices. The mining devices have access to the ledger orblockchain and therefore data recorded in the ledger or the blockchaincan be bought and sold by users operating the mining devices in thedecentralized network. In some embodiments, one or more of the miningdevices can be configured to create tokens that can be bought, sold, ortraded. The tokens can be non-fungible tokens (NFTs) each of which isunique and can correspond to unique data recorded in the ledger orblockchain. As mentioned above, the consensus score of a mining devicecan be based on the uniqueness of the ADS-B or Remote ID data that amining device receives. As an example, node 206 might receive ADS-B orRemote ID data from an aircraft carrying foreign dignitaries. The ADS-Bor Remote ID data associated with the aircraft carrying the foreigndignitaries is unique, because the people on the aircraft are foreigndignitaries and it likely is not a commercial airliner. As a result, theowners or operators of the mining device making up the decentralizednetwork may have their mining devices configured to determine the ADS-Bor Remote ID data that coincides with important people, such as foreigndignitaries, and can generate a NFT associated with ADS-B or Remote IDdata received from the aircraft carrying the people on the aircraft. Themining devices can determine whether the ADS-B or Remote ID dataproduced by the aircraft coincides with foreign dignitaries from acertain country by analyzing media outlet data or social media platformsto determine when foreign dignitaries are scheduled to fly from oneplace on the globe to another, and purchasing data associated with aflight path or trajectory that the aircraft has taken between the pointof origin and its destination. If the point of origin and point ofdestination, or the traveled flight path, coincides with the informationobtained from media outlets or social media about the foreigndignitaries travels, then the mining devices can determine that theADS-B or Remote ID data that was broadcast by the aircraft, and recordedto the ledger or the blockchain, coincides with the travel plans of theforeign dignitaries and can generate a NFT associated with the ADS-B orRemote ID data recorded to the ledger or blockchain. The data recordedin the ledger or blockchain can be referred to as a data marketplace214. Because the node 206 received the real-time un-encrypted ADS-B orRemote ID data 208, and the real-time un-encrypted ADS-B or Remote IDdata 208 is recorded to the ledger or blockchain, node 206 can verifythat it was the node that received the data because it was encryptedwith its private key 212. As a result the node 206 can decrypt thereal-time un-encrypted ADS-B or Remote ID data 208 recorded to theledger or blockchain, using private key 212, to verify that it was thenode that received the real-time un-encrypted ADS-B or Remote ID data208.

In some embodiments, some mining devices can be trusted custodians (TCs)of the ledger or the blockchain. TCs are trusted mining devices thathave mined and or staked a predetermined level of relatedcryptocurrency, fiat currency, or minted NFTs, asset coins, assettokens, in the ledger or blockchain. TCs can selectively mint NFTs fromADS-B, Remote ID, or AIS data stored on the related ledger orblockchain. In some embodiments, TCs can also use their access to realtime data being validated to generate virtual geo-avatars in virtualenvironments such as virtual reality, augmented reality, video games,gambling, etc. Contributing Users who have tracked transportationvehicles will have a private key for each asset tracked and when dataassociated with that transportation vehicle is exchanged for value onthe blockchain through purchasing, minting, auctioning, forging, fees,etc. the “contributing users” who have the associated private key willbe compensated with a relative amount of cryptocurrency through themarketplace, following the “rules and regulations” programmed into themining smart contract such as future commissions on minted NFTs,percentage share per asset for proof of stake holders, etc.

Contributing users can be any nodes or mining devices involved in thetracking of a transportation vehicle from a point of origin to a pointof destination. Each contributing user can have their own private keyassociated with their portion of data from the aggregate of flight data.The contributing users can opt in to benefit from the marketplace usingtheir private key. If opted in, when an NFT is minted by a TC on theblockchain the contributing user's private key will be exchanged. Thecontributing user's private key associated with the related flight datawill be transferred to the TC for them to retain digital asset ownershipof all the flight data as their own, by paying a relative amount oftoken or currency to the private key holders. If opted out, when an NFTis minted by a TC, the digital ownership will just not include theprivate key info or flight data from that user's miner.

In some embodiments, Contributing Users will also receive compensationwhen their public encrypted data is used by third party cloud servicesproviders and HE based data processing platforms for analyzing globalledger or blockchain transportation vehicle data in real time orhistorically without compromising the data unique values. There will bea higher compensation for sharing the public key and furthercompensation for sharing the private key, which public users can use todecrypt the f(x) data on the public blockchain for further analysis.

In some embodiments, the ADS-B, Remote ID, or AIS data of transportationvehicles tracked using proof of reception methods and their associatedblockchain signatures, data, fingerprint, etc. can be transacted forcryptocurrency or fiat currency. Real time ADS-B, Remote ID, or AIS datacan be selected for purchase by TCs who have staked a related amount ofcrypto or fiat currency or equivalent value in NFT, etc.

In some embodiments, data can be bought as historical blocks or througha “gas fee”, which increases the market value of all compensation, newlygenerated “asset coins” of which there are a limited supply, for thetransportation vehicles that are tracked, monetarily corresponding totheir “uniqueness value” to access real time data from transportationvehicles as blocks are being validated, and the demand of the overall“gas” supply of buyers and sellers.

A gas fee, or a blockchain transaction fee, is fee paid by the miningdevices to the validator nodes for their services on the blockchain. Atcertain times there is a high demand for computation, such as mintingNFTs or transferring tokens between wallets, and the gas fee dynamicallyadjusts to be higher. This increase is due to validators on the networkdemanding a higher price for mining or computing on the network leadingto increased gas fees for the whole network. The gas fee of the datamarketplace 214 can be determined by the amount of wallets which areminting, buying, selling NFTs, or buying HE data, and the magnitude ofthe price for which they are being minted or transacted for.

In some embodiments, historical data associated with tracking of atransportation vehicle over the course of transportation vehicle's pathof travel that corresponds to an initial and final block of theblockchain can be purchased with a delay of a period of time aftervalidation and consensus of the block is achieved and is published tothe blockchain as HE Signed Data f(x) 222. Public homomorphicallyencrypted data and the associated private keys of mining devices (nodes)that have opted to share their associated private keys in addition tothe private keys of the TCs that have also opted to share their privatekeys, can be bought through auction, tender, negotiation, etc. inaccordance with some embodiments.

FIG. 4 illustrates an exemplary network 400 for transmitting andreceiving homomorphically encrypted transportation vehicle data that isgenerated by an airplane 302, received by a mining devices 304 and 308and validated by validator 306 in accordance with some embodiments. Realtime position data 312 produced by airplane 302 can be broadcast over acertain footprint of the ground over which is flying, and the real timeposition data 312 can include ADS-B data that is received by a miningdevice 304. The mining device 304 can apply a homomorphic encryptionalgorithm to the received real time position data 312 using a uniqueprivate key associated with the mining device 304 and transmit the realtime position data 312 that has been homomorphically encrypted to thevalidator 306 via the internet 310. The mining device 308 can alsoreceive the real time position data 312 and can similarly apply ahomomorphic encryption algorithm to the received real time position data312 using a unique private key associated with the mining device 308 andtransmit the real time position data 312 that has been homomorphicallyencrypted to the validator 306 via the internet 310.

The validator 306 can receive the real time position data 312 that hasbeen homomorphically encrypted from the mining devices 304 and 308 andcan analyze the homomorphically encrypted real time position data fromthe mining devices 304 and 308 and compare it to homomorphicallyencrypted data from other trusted mining devices that are in the samegeographic area of the mining devices 304 and 308 to determine whetherthe homomorphically encrypted real time position data received by themining devices 304 and 308 is valid. This can be referred to as a peerconsensus protocol 336. The validator 306 can publish thehomomorphically encrypted real time position data received by the miningdevices 304 and 308 to the ledger or blockchain, after the validator 306applies a validation signature operation to the homomorphicallyencrypted real time position data received by the mining devices 304 and308.

FIG. 5 is a flow diagram illustrating the process of validatinghomomorphically encrypted transportation vehicle data and generating aconsensus score for a mining device, in accordance with some embodimentsof the disclosure. The process 500 includes an untrusted mining node(black box node (un-trusted) 406), a validator node 410, and a trustedmining node (black box nodes (trusted) 414). The untrusted mining node,validator node 410, and trusted mining node form a decentralizednetwork. The untrusted mining node may receive transportation vehicleinformation such as ADS-B, AIS, Remote ID data 402 that is broadcastfrom an airplane, drone, or ship, boat, or vessel via antenna 404 thatis in transit from a point of origin to a point of destination. In someembodiments, the untrusted node can homomorphically encrypt the ADS-B,AIS, Remote ID data 402 into a homomorphically encrypted smart contract408 and transmit the homomorphically encrypted smart contract 408 to avalidator node 410 that will compare the data in the homomorphicallyencrypted smart contract 408, corresponding to the ADS-B, AIS, or RemoteID data 402, to regulatory data 412 corresponding to the point oforigin, point of destination, weather, wave buoy, or flight routinginformation in order to determine whether the ADS-B, AIS, Remote ID data402 received by the untrusted mining node is valid. The validator node410 can also compare data received by the trusted mining node to thedata in the homomorphically encrypted smart contract 408, and determinewhether the ADS-B, AIS, or Remote ID data 402 received by the untrustedmining node is valid. If the validator node 410 determines that theADS-B, AIS, or Remote ID data 402 is valid, the validator node canbroadcast a trusted consensus score 420 associated with the trustedmining device to the untrusted mining node. The validator node 410 canthen apply a validation signature operation to the homomorphicallyencrypted smart contract 408, and publish a signed version of thehomomorphically encrypted smart contract 408 to the public ledger 416.The data recorded in the public ledger 416 can be referred to as amarketplace 418 in accordance with some embodiments.

FIG. 6 is a flow diagram illustrating a process 600 of applying anoperation to homomorphically encrypted transportation vehicle data, inaccordance with some exemplary embodiments of the disclosure. The client502 can be a mining device that homomorphically encrypts data (x)associated with a transportation vehicle that is in transit intoencrypt(x) 512. The data (x) can be data associated with atransportation device that should remain private. For instance, the data(x) can be ADS-B, AIS, or Remote ID data as mentioned above, that isreceived by the client 502. The client 502 transmits encrypt(x) 512 tothe server 504 which is a validator node similar to a validator node 410in FIG. 4 or validator node 608 in FIG. 6 that is capable of applying ahomomorphic signature operation to encrypt(x) 512. The server 504 canapply a homomorphic operation, or compute a homomorphic function f( )514 on encrypt(x) 512 such that the resulting value of the applicationof the function 514 to encrypt(x) 512 is homomorphically similar toencrypting the function f( ) 514 being applied directly to the data (x).That is the application of function f( ) 514 to encrypt(x) 512 ishomomorphically similar to encrypt(f(x)) 534. The server 504 cantransmit encrypt(f(x)) 534 to client 502, and the client 502 can decryptencrypt(f(x)) 534 into f(x) 532 because the client 502 used its privatekey to generate encrypt(x) 512 by encrypting data (x).

Because the client 502 homomorphically encrypts data x (encrypt(x) 512),and the server 504 applies the homomorphic function f( ) 514 toencrypt(x) 512, when the client 502 decrypts encrypt(f(x)) 534 intof(x), the client 502 can view the results of the application ofhomomorphic function f( ) 514 to the data (x). Furthermore because theclient 502 homomorphically encrypts data (x) with its own private key,the server 504 cannot recover the data (x). As a result, this providesthe users or operators of the client 502 with the ability to participatein ownership of the data (x) without compromising the privacy of thedata (x) while using untrusted machine learning models upon sensitivedata (x).

The client 502 can securely transfer sensitive data can to the server504, which may not be a trusted party, and request the server 504 toprocess and/or analyze the sensitive data according to a set ofparameters provided to the server 504 by the client 502. Because thesensitive data (x) is homomorphically encrypted and the requestedprocessing and/or analysis of the sensitive data involves theapplication of a homomorphic function to the sensitive data by theserver 504, the server 504 will not have access to the sensitive data.

In some embodiments, encrypt(x) 512 can be analyzed by the server 504using privacy preserving neural network methods and a signed validationoperation such as f( ) can be performed. This signed data f(x) 532 doesnot reveal the intricacies of encrypt(x) 512, but retains enoughstructure that validator nodes in the decentralized network can analyzethe data in the signed data f(x) 532 to verify its authenticity andvalidity in relation to other data sources. The (x) data will beconverted to f(x) by each miner, transmitted to the network, verified byvalidators, and f( ) is further operated upon to sign the data f(x) asvalid or invalid before writing to the blockchain.

In some embodiments, mining devices, other than client 502, can accessthe signed data f(x) 532 on the blockchain and apply one or moreoperations to the signed data f(x) 532 such as applying neural networkweights to the signed data f(x) 532, thereby transforming the signeddata f(x) 532 into another value g(f(x)). The mining devices, other thanclient 502, can publish g(f(x)) to the blockchain or internetrepositories such as Github. However without the private key of theclient 502, the mining devices other than client 502, cannot recoverg(f(x)) thereby keeping the data (x) private. Only the client 502 canuse its private key to decrypt g(f(x), and in combination with thesignature of the validator who signed the data by applying the functionf( ) to the data (x), the client 502 can recover g(x) which is aweighted version of the data (x) in accordance with some embodiments.

In some embodiments, the client 502 will have a democratic miningalgorithm based upon the use of homomorphic encryption and neuromorphiccomputing techniques. Data such as ADS-B, AIS, or Remote ID data that isreceived via sensors or antennas communicatively coupled to the client502, is validated by a neuromorphic processor in the client 502 and canbe homomorphically encrypted with a private key unique to the client502, or a wallet associated with the client device 502 in addition to apublic key.

FIG. 7 is a flow diagram illustrating a process 700 of validatinghomomorphically encrypted transportation vehicle data using ahomomorphic signature operation, in accordance with some embodiments ofthe disclosure. Process 700 can include a black box node 602, which canbe a trusted mining device or an untrusted mining device, and avalidator node 608. The black box node 602 can apply a homomorphicencryption operation to transportation vehicle data such as ADS-B, AIS,or Remote ID data that it receives from a transportation vehicle. Theblack-box node 602 applies the homomorphic encryption operation totransportation vehicle data using its private key 616. In someembodiments, the black-box node 602 can share the private key 616 withother mining device in a marketplace 614, which includes mining devicesof the decentralized network. The black-box node 602 can send, or share,the private key 616 to the marketplace 614 if the black-box node 602wants to share the transportation data that it received with othermining devices in the marketplace 614. The mining devices in themarketplace 614 can use the private key to decrypt the transportationdata that was received by the black-box node 602 and that is recorded inthe asset-token public ledger 612. The public key 606 can be shared withthe validator node 608 which can be used by the validator node 608 toencrypt messages that it has to send to the black-box node 602. Thevalidator node 608 can also receive the transportation vehicle data fromthe black-box node 602 and apply a validator homomorphic signatureoperation 610 to the transportation vehicle data. The validator node 608can sign the transportation vehicle data, using the validatorhomomorphic signature operation 610, with a signature that indicates tomining devices that have access to the asset-token public ledger 612that the transportation vehicle data received from the black-box node602 is valid. The validator node 608 can sign the transportation vehicledata, using the validator homomorphic signature operation 610, with asignature that indicates to mining devices that have access to theasset-token public ledger 612 that the transportation vehicle datareceived from the black-box node 602 is invalid. After the validatornode 608 signs the transportation vehicle data, homomorphically signedand homomorphically encrypted transportation vehicle data can bepublished to the asset-token public ledger 612 by the validator node608.

FIG. 8 is an image of a prototype mining device, in accordance with someembodiments of the disclosure. In some embodiments, the prototype miningdevice 800 can include a field programmable gate array (FPGA) devicethat includes a system on chip (SoC) module 702 that emulates aneuromorphic processor, a decoder 706 that is used to decodetransportation vehicle data signals, and a secure electronically wiredenclosure 704 that prevents physical tampering with mining device 800.In some embodiments the system on chip (SoC) module 702 can be a TrenzElectronics Zynqberry Emulating IBM's TrueNorth Neuromorphic Chip, thedecoder 706 can be RTL-USB software defined radio (SDR) Decoder, and theenclosure 704 can be a Zymbit Secure hardware module.

In some embodiments, the SoC module emulating the neuromorphic processorcan monitor physical tampering to the mining device 800 through JTAG,USB, or PCI. The enclosure 704 can be powered independently by a battery(e.g., a watch battery) that detects physical tampering of the enclosurein response to an embedded wire being severed. Secure physicallytamper-resistant hardware can prevent many geographic tampering andspoofing attacks, while enabling only verified legitimate users, oroperators, of the mining device 800 to add validated transportationvehicle data to the decentralized and published to the public ledger orblockchain. In some embodiments, if tampering is actively detected, suchas modification to any of the components of mining device 800, this cancause the consensus score of the mining device 800 to be adverselyaffected, and the consensus score of the mining device 800 can bepublished to the ledger of the blockchain amongst the validationsignature, so all mining devices including validator nodes and TCs willbe aware of the change to the consensus score of the mining device 800.This causes the validator nodes to adjust their consensus determinationdynamically as the mining device 800 becomes less trusted due tohardware modification or software hacking. If tampering detection issevere enough, the mining device 800 can have its consensus score resetto zero or permanently terminated from adding or publishing data to theledger or blockchain. Furthermore a penalty multiplier can also be addedto the consensus score of the mining device 800 to amplify and preventany future spoofing. Any gross hardware modifications can be detected byan electrical fault created by cutting of embedded circuitry in theenclosure 704.

Any removal of the real-time clock management, must be performed whilethe mining device 800 remains powered on, otherwise physical or softwaretampering is possible, and will result in resetting of the consensusscore for the mining device 800 to zero, with a permanently severepenalty multiplier following consensus mechanisms such as Proof ofAuthority, Proof of Trust, Proof of Rec, Proof of Stake, Blockchainreputation based consensus (BRBC), etc. In some embodiments, theenclosure 704 can include anti-tampering passive mechanisms such asscrews, shearing construction, adhesives, etc. in addition to activemechanisms such as electrical circuitry that is broken when the deviceis opened without deactivating the active detection mechanisms. In someembodiments, the neuromorphic processor of the device can detect activetampering or side channel attacks of the physical circuitry, components,etc. In addition to detection of digital tampering such as attacks onthe ports JTAG, USB, Ethernet, on the mining device 800, theneuromorphic processor can also analyze incoming sensor data from thedecoder 706 via a wireless or wired connection of an antenna for exampleto determine if spoofing or errors are present to prevent erroneous datafrom being transmitted to the decentralized network for validation by avalidator node.

As shown in FIG. 9 , in some embodiments mining device 900 includes aneuromorphic chip 808 (neuromorphic processor), and a physical enclosure812 between the neuromorphic chip 808 and the external connectivity 806that is communicatively coupled to the sensor 802 and the Internet 804through one or more wireless or wired connections. The mining device 900also includes one or more peripherals 810 that can be TCP/IP, HDMI, PCBconnections from the neuromorphic processor to buttons, LEDs, monitor,Bluetooth, etc. The peripherals provide additional functionality to themining device. The neuromorphic processor is still checking the validityof the incoming and outgoing peripherals signals to the mining device toprevent tampering or spoofing. Transportation vehicle data can bedetected by the sensor 802, and can be transmitted by the sensor 802, tothe external connectivity 806, which is external to physical enclosure812, via wireless connection (e.g., IEEE 802.11 or Cellular) or a Wiredconnection (e.g., Ethernet). In some embodiments, the receivedtransportation vehicle data is sent to the neuromorphic chip 808 fromthe external connectivity 806, and the neuromorphic chip 808 can verifywhether the received transportation vehicle data has been tampered withafter being detected by the sensor 802. The neuromorphic chip 808analyzes the received transportation vehicle data before it transfersthe received transportation vehicle data to the one or more peripherals810 for further analysis and processing in some embodiments. Theneuromorphic chip analyzes the incoming sensor data to check if valuesare within thresholds set by the mining device's firmware, establishedduring manufacturing or through firmware updates. The data packets,electrical signal characteristics, and antenna values will be analyzedto be within thresholds set to prevent spoofing and tampering attacks.ADS-B data for example should be 120 bits with a 8 bit preamble and 112bits of information about an aircraft. Information about the aircraftshould further be within thresholds set by the physical limitations ofaircraft types. These can include altitude and speed which can be usedfor thresholding the validity of data, for example a Cessna 172 shouldnot be broadcasting from 100,000 feet in altitude and therefore, theneuromorphic processor would flag this input before appending to theblockchain network. Additionally, a vehicle at a specific latitude andlongitude with altitude, should not be able to move to another latitude,longitude, altitude combination that does not fit within the constraintsof the aircraft type's speed or range. These thresholds can beimplemented into the neuromorphic processor as a pre-check beforefurther processing incoming signal data. Validator nodes of the networkwould then use multiple receiver's data to determine if the Cessna 172broadcasting at 100,000 feet was a consensus of multiple receivers or anerroneous decoding from an independent receiver.

The one or more peripherals 810 can transfer the analyzed and processeddata back to the neuromorphic chip 808 for final validation andhomomorphic encryption. The neuromorphic chip 808 can homomorphicallyencrypt the analyzed and processed data, and then send thehomomorphically encrypted analyzed and processed data to the externalconnectivity 806 while simultaneously detecting whether thehomomorphically encrypted analyzed and processed data is being tamperedwith as it is being transferred to the external connectivity 806. Thehomomorphically encrypted analyzed and processed data is transmitted bythe external connectivity 806 to the internet 804 via a wireless orwired connection.

FIG. 10A is a flow diagram illustrating a deep leaning reinforcementprocess 1000 for analyzing real-time transportation vehicle data topredict trajectory of the transportation vehicle, in accordance with thesome embodiments of the disclosure. The neuromorphic processorsdisclosed herein can execute one or more deep reinforcement learningtechniques to analyze all the available transportation vehicle data,such as aircraft, drones, marine vessels, etc. and assign a uniquefingerprint to the transportation vehicle data associated the particulartransportation vehicle (aircraft, drones, marine vessels). Theneuromorphic processor can implement the one or more deep reinforcementlearning techniques as an agent 904 that performs certain actions 908 ina given environment, which includes the physical space of real-time andhistorical transportation vehicle data that has been processed by theneuromorphic processor. The real-time and historical transportationvehicle data can include ADS-B, AIS, or Remote ID data.

Actions such can be performed at a certain state in a certainenvironment or time. An action at certain time can change theenvironment and the state to create a new action opportunity at a latertime. If the action created a reward from the environment for the agent,then the agent will try to replicate actions similar, while it will dothe opposite for actions that do not result in a reward.

For prediction of tracking of aircraft, the agent's state can be thecurrent positional information from available aircraft, or environment.Actions by the agent can be calculating a prediction of the next speed,altitude, heading. These agent's predicted calculation actions will havean error from the next broadcasted real world positional values of anaircraft. These real world values can be used to update the next stateof the agent's environment and determine if the agent's prediction wasaccurate to within an error threshold. If the error is reduced than theagent is rewarded, while if the error is large, the agent is notrewarded to motivate it to make more accurate action predictions.

In the case of an aircraft, even if the real time data is limited orblocked, the agent 904 can seek a solution for obfuscated aircraft datathrough one or more actions 908 to search the feature space to determinethe associated missing registration information given the availabledata. The available data can be data stored in the 1018 CentralDatabase, such as FAA repositories. In addition, publicly availablephotos and videos of aircraft which contain aircraft registrationnumbers and associated geodata. The missing registration information canalso be determined based on uncertainty weights. The uncertainty weightscan be the error of the agent's action, such as predicting next positionor an obfuscated aircraft's actual associated registration, vs. theupdated available real world data fed into the environment.

The agent 904 can operate in an environment 910 which can be all thecurrent aircraft data in the central database 1018 and other airplanedata. The state can be all of the positional information associated withthe environment at different times.

This is where the agent 904 can make a decision or take an action 908and the environment 910 will respond with the consequence of that action908, such as the given transportation vehicle data and one or moreassociated actions 908 not correlating and resulting in an impossiblematch. Thereby reinforcing the agent 904 to make more accurate decisionsand predictions, until fully trained upon a given feature space. For theexample of aircraft, and other vehicles, they are unable to be in twolocations simultaneously, and therefore the history of an aircraft canbe traced from its first flight into FAA airspace and to any airport inthe globe that has ADS-B coverage, without the need for any datacollection between the two destinations. For each aircraft with adequatehistory and training, a related reinforcement agent based model can bedeveloped upon the operational signature of the aircraft. This can beused to predict future actions and derive meaning from prior historicaldata for further analysis.

If the one or more actions 908 taken by the agent 904 are correct, theenvironment 910 can issue a reward 906 to the agent 904 to reinforcesimilar actions by the agent 904 in the future. The state can be all ofthe positional information associated with the environment at differenttimes. In addition to the current data in the central database.

FIG. 9B is a flow diagram illustrating a process 1000 of training aneural network to analyze real-time transportation vehicle data topredict trajectory of A transportation vehicle, in accordance with thesome embodiments of the disclosure. The tensor of asset features 914 canbe a n-dimensional array of the asset data 920 where n is the number ofdata points tracked in real time. This tensor could be a 3D tensor withan explicit time axis associated with the positional data.

The multi-dimensional tensor is fed into the neural network 912. Theneural network can be trained using data from historical asset data tohave prediction weights 916 for calculating a predicted future assetdata value, and measuring the error 918.

The neuromorphic processor can implement a neural network 912 thatgenerates one or more predictions weights 916 that are used to predicttransportation vehicle position and time data of a transportationvehicle based on all real time asset data 920 and historicaltransportation vehicle data associated with different transportationvehicles. The neuromorphic processor can compare the all real time assetdata 920, which includes position and time data of the differenttransportation vehicles, to the predicted transportation vehicleposition and time data and determine error between the real vs predicted918 transportation vehicle data. The error between the real vs predicted918 transportation vehicle data is fed back into the neural network 912which can adjust the prediction weights 916 to improve futurepredictions of the position and time data of the differenttransportation vehicles, by selecting prediction weights 916 thatminimize the error between the real vs predicted 918 transportationvehicle data.

FIG. 11 is a flow diagram illustrating a process for integratingreal-time transportation vehicle artificial intelligence data with andpublicly available data to generate a fingerprint associated with atransportation vehicle, in accordance with some embodiments of thedisclosure. Process 1100 can include FAA swift data 1006, Live blockedADS-B data 1004, ADS-B satellite data 1002 each of which can be storedin one or more repositories or databases associated with thedecentralized network. The system can further include an antenna node1008 that receives live antenna data 1010 from transportation vehicles.The live antenna data 1010, FAA swift data 1006, Live blocked ADS-B data1004, ADS-B satellite data 1002 can all be deposited into centraldatabase 1018. An error (blocked—unblocked error 1016) between the Liveblocked ADS-B data 1004 and ADS-B satellite data 1002 can be calculatedby the reinforcement learning agent. The reinforcement learning agent1014 can be a randomly selected node's neuromorphic processor or acombination thereof such as fog computing, where a central processorusing secure cloud computing resources aggregates the data from all ofthe randomly selected nodes.

and the blocked—unblocked error 1016 can be used by a reinforcementlearning agent 1014 to improve the predictions 1012 associated withposition and time data related to the transportation vehicle. Thepredictions 1012 can be fed back into the Live blocked ADS-B data 1004and Condor Terminal 1020. Also the live antenna data 1010, FAA swiftdata 1006, Live blocked ADS-B data 1004, ADS-B satellite data 1002 canalso be accessed by the Condor Terminal 1020. Additional data sourcessuch as live optical data 1028, data produced by digital camera nodes1026, data produced by remote sensing nodes 1030, social media data1024, and plane spotting data 1022 can also be stored in centraldatabase 1018 and accessible by the Condor Terminal 1020.

The live optical data 1028 captured by digital camera nodes 1026 andremote sensing nodes 1030 be analyzed using optical characterrecognition to determine information associated with the transportationvehicle such as registration information that identifies who owns thetransportation vehicle. The live optical data 1028 can include imageryor video of the transportation vehicle. In some embodiments, imagery orvideo of the transportation vehicle can be obtained from the socialmedia data 1024 or news which may include registration informationassociated with the transportation vehicle. Once the owner of thetransportation vehicle has been determined, a fingerprint can beassigned to the transportation vehicle, and the fingerprint can beutilized to build predictions upon where the aircraft will travel tonext with high certainty. After a transportation vehicle has beenfingerprinted successfully and this prediction is validated throughfurther analysis of future trips taken by the transportation vehicle,this transportation vehicle can be used to reliably confirm theconsensus score of untrusted mining device's transmissions.

FIG. 12A is a flow diagram illustrating the process 1210 ofautomatically generating an asset token, and FIG. 12B is flow diagramillustrating the process 1200 of manually generating an asset token.There are many “ordinary” asset movements, such as airplane flights thatoccur daily, while there are also many unique or special flights such asemergency landings, airport diversions, customized flight plans, etc.There can also be public drone, aircraft, spacecraft flightstransmitting geospatial data publicly while traveling throughout theworld; in addition to marine vessels, ground based vehicles such asrobots, cars, tracked vehicles, etc. Due to the vast amount of flightsand other transportation events that occur daily, an automated methodfor assigning uniqueness and specialty can be used in order to determinewhich transportation vehicle data (Asset Coins) should be automaticallyforged into a non-fungible token for public auction (Asset Token).

There can be a limited supply of Asset Coins in order to enable scarcityand prevent inflation of the inherent value which is related to the gasfee and the amount of currency being exchanged for Asset Coins andaccess to real time blockchain formation or historical data. The amountof Asset Coin distributed can be “halved” over a period of time wherethe reward is decreased over time, as the price increases. This can bedetermined automatically, manually, or through miner group votingconsensus. Interactions between validated positional data can be used toautomatically generate related “Child tokens” from the reaching of athreshold proximal distance or time period in which two or more assetshave “interacted” with each other in time, space, or through their ownsensor interactions such as Bluetooth, radar, ultrasonic, etc. This canbe used to create child tokens from two unique transportation vehiclesapproaching orthogonal points in time or space at the same or differenttime such as being on opposite sides or other orthogonal relationshipsof the earth at the same time, or in the same position but exactly afixed time period such as 24 hours later. Manually determining whichflight data and the resultant flight coins minted should be forged intoa non fungible token (NFT) (Asset-Token) is a tedious process and can beautomated using artificial intelligence.

A matrix calculation can be performed by the reinforcement learningagent 1014 for assigning a weight towards a given transportation vehicleposition and time data that should be selected for forging of a NFT. Theweight can be determined by various factors such as for example, flightroute frequency, related news media exposure, aircraft owner, flightroute unique pattern, etc. The B737 and A320 are the most commonaircraft and therefore these are the lowest of rarity, while businessjets such as Gulfstream 650 aircraft are more rare in their frequency,increasing their NFT uniqueness weighting. Common routes betweendestinations for logistics and transportation are commonly repeatedlytraveled such as NYC to London, while rare flights such as Beverly, MAto Dubai, UAE are less likely to occur naturally and are therefore moreunique. Exposure tools on social media are able to quantify the level ofengagement or “impressions” with certain news information or posts,which can be used to quantify in the matrix the public exposure to theflight data such as a passenger disruption causing a flight to divert,events leading to crash or accident, or engagement on social media. Theowner of the aircraft and the related flight data has a weighting on theuniqueness matrix, where a high profile public figure on an aircraft ismore unique than an aircraft owned by a non-public figure. The effect ofgeopolitical scenarios such as an airlift from a warzone or notableweather systems can effect flights aggregately. This aggregate effectitself can be minted into a non-fungible token representing the shift orchange from prior historical data (e.g. flights being diverted around acountry due to geopolitical factors) can be auctioned as a unique NFT.All of these variables will be considered when determining which of thethresholded daily flights should be converted to NFT in real time uponflight completion. Forging of NFTs will be limited to a specific numberper day at a fixed price, where trading of the asset can occur, but ateach transaction there is a fee incorporated that returns a percentageof the traded asset value towards the mining devices that received thetransportation vehicle position and time data.

As shown in FIG. 112 the process 1210 shows how asset data 1112(transportation vehicle data) can be ranked by a uniqueness rankingalgorithm 1118 in comparison to other asset data, and if the asset data112 is ranked high enough the asset data 1112 can be minted inaccordance with NFT minting ERC-721 1120. The minted asset data 1112 canbe published to the public ledger or public blockchain 1114 along withthe asset data 1112. And the minted asset data 1112 is made available ina marketplace 1116. In FIG. 112 asset data 1102 can be published to thepublic ledger or public blockchain 1104 and the mining device thatreceived the asset data 1102 can verify its private key by decryptingthe homomorphically encrypted asset 1102. If the mining device wants tothe owner of the private key using currency from their associatedwallet. After the mining fees have been paid, the asset data 1102 can beminted in accordance with NFT minting 1112, and the minted asset data1102 can be published to the public ledger or public blockchain 1104along with the asset data 1112. And the minted asset data 1112 is madeavailable in a marketplace 1106.

FIG. 13 is a flow diagram illustrating the process 1300 of adding aminted token to a market place, in accordance with exemplary embodimentsof the disclosure. The process 1300 can begin with the weather proofcoaxial wifi transmitter 1204 receiving ADS-B data 1202 on antenna 1236.The ADS-B data 1202 can be transmitted by the weather proof coaxial wifitransmitter 1204 to the Wi-Fi 1216 receiver which is communicativelycoupled to the black-box miner 1214. The black-box miner 1214 caninclude a neuromorphic processor that executes asset-token miningalgorithm 1208, and is secured by off the shelf hardware security module1206. A verified user or owner of the black-box miner 1214 can interactwith the black-box miner 1214 user interface 1212 via one or more of thedevices 1210 that include a mobile phone application that can beconnected to user interface 1212 via an internet connection usingTCP/IP, a TV/monitor that can be connected to the user interface 1212via a HDMI connection, or a Bluetooth and LED device that can beconnected to the user interface 1212 via a PCB.

The black-box miner 1214 can homomorphically encrypt the ADS-B data 1202by applying the asset token mining algorithm 1208 to the ADS-B data 1202thereby creating an asset-token miner smart contract 1218 which can bepublished to an asset-token public ledger 1220 after a validator node ortrusted black-box miner validates the homomorphically encrypted ADS-Bdata 1202. The homomorphically encrypted ADS-B data 1202 that ispublished to the asset-token public ledger 1220 can be published to amarketplace 1228. Token buyers 1222 can be purchasers of non-fungibletokens which have been minted on the marketplace 1228. Previously,marketplace 1106 and 1106 show how NFTs are minted.

FIG. 14 is a flow chart of a process 1400 for processing position andtime data at a mining device, in accordance with some embodiments of thedisclosure. The process 1400 can begin at block 1402, where a miningdevice receives one or more signals corresponding to a transportationvehicle. The one or more signals can include ADS-B, AIS, or Remote IDdata. The mining device can include a neuromorphic processor that iscommunicatively coupled to a radio that receives the one or moresignals. The mining device can decode the first position data and thefirst time data from the one or more received signals at block 1404, andthen determine that the first position data and first time data do notinclude errors at block 1406. After determining that there are no errorsin the first position data and the first time data, the mining device,and more specifically, the neuromorphic processor can apply ahomomorphic encryption function to the first position data and the firsttime data at block 1408. At block 1410 the mining device can transmitthe homomorphically encrypted first position data and first time data toa validation device.

FIG. 15 schematically depicts an example network environment 1500 thatthe surgical robotic system can be connected to in accordance with someembodiments. Computing device 1518 can be used to perform one or moresteps of the methods provided by example embodiments. The computingdevice 1518 includes one or more non-transitory computer-readable mediafor storing one or more computer-executable instructions or software forimplementing example embodiments. The non-transitory computer-readablemedia can include, but are not limited to, one or more types of hardwarememory, non-transitory tangible media (for example, one or more magneticstorage disks, one or more optical disks, one or more USB flashdrives),and the like. For example, memory 1506 included in the computing device1518 can store computer-readable and computer-executable instructions orsoftware for implementing example embodiments. The computing device 1518also includes the processor 1522 and associated core 1504, for executingcomputer-readable and computer-executable instructions or softwarestored in the memory 1506 and other programs for controlling systemhardware. The processor 1522 can be a single core processor or multiplecore (1504) processor.

Memory 1506 can include a computer system memory or random accessmemory, such as DRAM, SRAM, EDO RAM, and the like. The memory 1506 caninclude other types of memory as well, or combinations thereof. A usercan interact with the computing device 1518 through the display 1502,such as a touch screen display or computer monitor, which can displaythe graphical user interface (GUI) 1539. The display 1502 can alsodisplay other aspects, transducers and/or information or data associatedwith example embodiments. The computing module 18 can include other I/Odevices for receiving input from a user, for example, a keyboard or anysuitable multi-point touch interface 1508, a pointing device 1510 (e.g.,a pen, stylus, mouse, or trackpad). The keyboard 1508 and the pointingdevice 1510 can be coupled to the visual display device 1512. Thecomputing device 1518 can include other suitable conventional I/Operipherals.

The computing device 1518 can also include one or more storage devices1524, such as a hard-drive, CD-ROM, or other computer readable media,for storing data and computer-readable instructions, applications,and/or software that implements example operations/steps of theprocesses as described herein, or portions thereof, which can beexecuted on processor 1522 and displayed on display 1512. Examplestorage devices 1524 can also store one or more databases for storingany suitable information required to implement example embodiments. Thedatabases can be updated by a user or automatically at any suitable timeto add, delete or update one or more items in the databases. Examplestorage devices 1524 can store one or more databases 1526 for storingprovisioned data, and other data/information used to implement exampleembodiments of the systems and methods described herein.

The computing devices 1518 can include a network interface 1512configured to interface via one or more network devices 1520 with one ormore networks, for example, Local Area Network (LAN), Wide Area Network(WAN) or the Internet through a variety of connections including, butnot limited to, standard telephone lines, LAN or WAN links (for example,802.11, T1, T3, 56 kb, X.25), broadband connections (for example, ISDN,Frame Relay, ATM), wireless connections, controller area network (CAN),or some combination of any or all of the above. The network interface1512 can include a built-in network adapter, network interface card,PCMCIA network card, card bus network adapter, wireless network adapter,USB network adapter, modem or any other device suitable for interfacingthe computing device 1518 to any type of network capable ofcommunication and performing the operations described herein. Moreover,the computing device 1518 can be any computer system, such as aworkstation, desktop computer, server, laptop, handheld computer, tabletcomputer (e.g., the iPad® tablet computer), mobile computing orcommunication device (e.g., the iPhone® communication device), or otherform of computing or telecommunications device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described herein.

The computing device 1518 can run any operating system 1516, such as anyof the versions of the Microsoft® Windows® operating systems, thedifferent releases of the Unix and Linux operating systems, any versionof the MacOS® for Macintosh computers, any embedded operating system,any real-time operating system, any open source operating system, anyproprietary operating system, any operating systems for mobile computingdevices, or any other operating system capable of running on thecomputing device and performing the operations described herein. In someembodiments, the operating system 1516 can be run in native mode oremulated mode. In some embodiments, the operating system 1516 can be runon one or more cloud machine instances.

The computing device 18 can also include an antenna 1530, where theantenna 1530 can transmit wireless transmissions a radio frequency (RF)front end and receive wireless transmissions from the RF front end.

While embodiments of the present disclosure are depicted and describedherein, it will be clear to those skilled in the art that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions will now occur to those skilled in the artwithout departing from the invention. It may be understood that variousalternatives to the embodiments of the invention described herein may beemployed in practicing the invention. It is intended that the followingclaims define the scope of the invention and that methods and structureswithin the scope of these claims and their equivalents be coveredthereby.

1.-13. (canceled)
 14. A validation device, the validation devicecomprising: a processor configured to or programmed to read one or moreinstructions held in memory to: receive encrypted first data from amining device, the first data including first position and first timedata for a transportation vehicle; receive or access second dataincluding second position and second time data for the transportationvehicle; determine validity of the first data by performing operationson the encrypted first data or on the encrypted first data and thesecond data to determine a relationship between the first data and thesecond data, and determine a validity of the first data by performingoperations on the encrypted first data or on the encrypted first dataand the second data to compare the encrypted first data and the seconddata; assign a consensus score to the mining device based at least inpart on the comparison of the first data and second data; apply asignature function to the encrypted first position and first time data,where the first position and first time data is determined to be valid,to obtain encrypted signed valid first position and first time data; andpublish the encrypted signed valid first position and first time data toa public transportation vehicle ledger.
 15. The validation device ofclaim 14, wherein the processor is configured to receive the secondposition and second time data from a trusted mining device.
 16. Thevalidation device of claim 14, wherein the processor is furtherconfigured to: receive the second position data and the second time datafrom a national or international repository of government regulatedtransportation vehicle data; and generate expected position dataassociated with the transportation vehicle for the first time based atleast in part on the received second position and time data; and whereinthe comparison of first data and the second data includes comparing theencrypted first position and time data with the generated expectedposition data for the first time.
 17. The validation device of claim 16,wherein the expected position and expected time data associated with thetransportation vehicle corresponds to a data point of an expectedtrajectory of the transportation vehicle.
 18. The validation device ofclaim 14, wherein the consensus score is further based at least in parton a volume of valid position and time data generated by the validationdevice.
 19. The validation device of claim 18, wherein the consensusscore is further based at least in part on a multiplier associated withoperating resources required for the transportation vehicle and auniqueness value associated with the transportation vehicle.
 20. Thevalidation device of claim 19, wherein: the multiplier is a first valuewhen the operating resources are associated with a first type oftransportation vehicle; the multiplier is a second value when theoperating resources are associated with a second type of transportationvehicle; and the first value is greater than the second value when theoperating resources associated with the first type of transportationvehicle are greater than the operating resources associated with thesecond type of transportation vehicle.
 21. The validation device ofclaim 19, wherein the uniqueness value is based at least in part on anunplanned path that the transportation vehicle takes between a point oforigin and a point of destination.
 22. The validation device of claim19, wherein the uniqueness value is based at least in part on anemergency associated with the transportation vehicle.
 23. The validationdevice of claim 19, wherein the uniqueness value is based at least inpart on a manifest associated with the transportation vehicle.
 24. Thevalidation device of claim 19 wherein the processor is furtherconfigured to generate a token based at least in part on the uniquenessof the transportation vehicle.
 25. The mining validation device of claim14, wherein the processor is further configured to: publish theconsensus score to the public transportation vehicle ledger.
 26. Thevalidation device of claim 14, wherein the first processor is configuredto transmit the encrypted first data for validation to be published to apublic blockchain.
 27. A non-transitory computer-readable medium storingcomputer-executable instructions stored therein, which when executed byat least one processor, cause the at least one processor to perform theoperations of: receiving encrypted first data from a mining device, thefirst data including first position and first time data for atransportation vehicle; receiving or accessing second data includingsecond position and second time data for the transportation vehicle;determining validity of the first data by performing operations on theencrypted first data or on the encrypted first data and the second datato determine a relationship between the first data and the second data;determining a validity of the first data by performing operations on theencrypted first data or on the encrypted first data and the second datato compare the encrypted first data and the second data; assigning aconsensus score to the mining device based at least in part on thecomparison of the first data and second data; applying a signaturefunction to the encrypted first position and first time data, where thefirst position and first time data is determined to be valid, to obtainencrypted signed valid first position and first time data; andpublishing the encrypted signed valid first position and first time datato a public transportation vehicle ledger.
 28. A validation method, thevalidation method comprising: receiving encrypted first data from amining device, the first data including first position and first timedata for a transportation vehicle; receiving or accessing second dataincluding second position and second time data for the transportationvehicle; determining validity of the first data by performing operationson the encrypted first data or on the encrypted first data and thesecond data to determine a relationship between the first data and thesecond data; determining a validity of the first data by performingoperations on the encrypted first data or on the encrypted first dataand the second data to compare the encrypted first data and the seconddata; assigning a consensus score to the mining device based at least inpart on the comparison of the first data and second data; applying asignature function to the encrypted first position and first time data,where the first position and first time data is determined to be valid,to obtain encrypted signed valid first position and first time data; andpublishing the encrypted signed valid first position and first time datato a public transportation vehicle ledger. 29.-35. (canceled)
 36. Thenon-transitory computer-readable medium of claim 27, wherein theprocessor is configured to receive the second position and second timedata from a trusted mining device.
 37. The non-transitorycomputer-readable medium of claim 27, wherein the processor isconfigured to: receive the second position data and the second time datafrom a national or international repository of government regulatedtransportation vehicle data; and generate expected position dataassociated with the transportation vehicle for the first time based atleast in part on the received second position and time data; and whereinthe comparison of first data and the second data includes comparing theencrypted first position and time data with the generated expectedposition data for the first time.
 38. The non-transitorycomputer-readable medium of claim 37, wherein the expected position andexpected time data associated with the transportation vehiclecorresponds to a data point of an expected trajectory of thetransportation vehicle.
 39. The non-transitory computer-readable mediumof claim 27, wherein the consensus score is further based at least inpart on a volume of valid position and time data generated by thevalidation device.
 40. The non-transitory computer-readable medium ofclaim 39, wherein the consensus score is further based at least in parton a multiplier associated with operating resources required for thetransportation vehicle and a uniqueness value associated with thetransportation vehicle.